LoL winner predictor

Introduction

Prepare the data

Open the data

file = '../raw_data/full_dump.json'
with open(file) as data_file:    
    data = json.load(data_file) 

data_keys = list(data.keys())
random.sample(data_keys, 5)

['NA Academy League/2018 Season/Summer Playoffs/Scoreboards/Semifinals and Finals_3_4',
 'Circuito de Leyendas Sur/2017 Season/Opening Season/Scoreboards/Week 6_4_2',
 'Challengers Korea/2020 Season/Summer Season/Scoreboards/Week 7_1_2',
 'Ultraliga/Season 5/Scoreboards/Week 6_6_1',
 '2016 International Wildcard Qualifier/Scoreboards/4-6_6_1']

f'input data: {len(data)}'

'input data: 35320'

Normalize the data

#Normalize means to expand the nested keys in the JSON file and the subsequent DataFrame.
##Normalize the complete dataframe and make a copy
df_normalized_teams = pd.json_normalize(data.values())
df_teams = df_normalized_teams.copy()
df_teams.sample(2)

	id	start	patch	winner	duration	picks_bans	teams.BLUE.name	teams.BLUE.total_turret_kills	teams.BLUE.total_inhibitor_kills	teams.BLUE.total_rift_herald_kills	...	teams.BLUE.side	teams.BLUE.players	teams.RED.name	teams.RED.total_turret_kills	teams.RED.total_inhibitor_kills	teams.RED.total_rift_herald_kills	teams.RED.total_dragon_kills	teams.RED.total_baron_kills	teams.RED.side	teams.RED.players
35189	CLS/2017 Season/Preseason Tournament/Scoreboar...	None	6.21	RED	2104	[]	Rebirth eSports	3	0	0	...	BLUE	[{'name': 'MANTARRAYA', 'id': 180979, 'role': ...	Last Kings	11	3	0	4	2	RED	[{'name': 'Nipphu', 'id': 185761, 'role': 'TOP...
14850	LCS/2019 Season/Summer Season/Scoreboards/Week...	None	9.12	BLUE	1690	[{'champion_name': 'Rumble', 'is_ban': True}, ...	Cloud9	11	3	1	...	BLUE	[{'name': 'Kumo', 'id': 214202, 'role': 'TOP',...	Clutch Gaming	0	0	0	1	0	RED	[{'name': 'Huni', 'id': 165378, 'role': 'TOP',...

2 rows × 22 columns

def get_patch_year(s):
    return int(s.split('.')[0]) + 2010

#return the year the game was played
df_teams['year'] = df_teams.patch.apply(get_patch_year)
df_teams[['patch', 'year']].sample(2)

	patch	year
17489	9.19	2019
27693	11.2	2021

#Make a dataframe dedicated to the team BLUE
df_normalized_BLUE = pd.json_normalize(df_normalized_teams['teams.BLUE.players'].explode())
df_BLUE = df_normalized_BLUE.copy()
df_BLUE.head(5)

	name	id	role	champion_name	champion_id	gold_15	kills_assists_15	deaths_15	total_gold	total_cs	total_kills	total_monster_kills	total_assists	total_deaths	total_damage_taken	total_damage_dealt	win	side
0	Kikis	172122.0	TOP	Nautilus	111	5326.0	8.0	1.0	12504	233	2	0	6	1	18220	156270	True	BLUE
1	Broxah	193072.0	JGL	Lee Sin	64	5261.0	11.0	1.0	12352	153	4	132	7	1	22212	152183	True	BLUE
2	Nisqy	185791.0	MID	Syndra	134	6009.0	13.0	1.0	13393	229	5	6	8	1	10647	177681	True	BLUE
3	MrRallez	183407.0	BOT	Jhin	202	5304.0	11.0	0.0	13969	315	2	5	9	0	9758	228328	True	BLUE
4	Klaj	171882.0	SUP	Karma	43	2767.0	12.0	0.0	9740	38	1	0	11	0	11917	34299	True	BLUE

#Make a dataframe dedicated to the team RED
df_normalized_RED = pd.json_normalize(df_normalized_teams['teams.RED.players'].explode())
df_RED = df_normalized_RED.copy()
df_RED.head(5)

	name	id	role	champion_name	champion_id	gold_15	kills_assists_15	deaths_15	total_gold	total_cs	total_kills	total_monster_kills	total_assists	total_deaths	total_damage_taken	total_damage_dealt	win	side
0	Phones	193289.0	TOP	Maokai	57	4528.0	2.0	7.0	9611	190	1	13	1	7	41065	111536	False	RED
1	Obvious	187241.0	JGL	Rengar	107	4728.0	2.0	1.0	9640	174	0	126	2	1	27879	147035	False	RED
2	MagiFelix	181359.0	MID	Ryze	13	4893.0	2.0	3.0	11840	301	0	2	2	3	16013	205899	False	RED
3	Sedrion	197437.0	BOT	Varus	110	5133.0	2.0	1.0	12010	283	2	1	0	1	10370	184927	False	RED
4	Noxiak	185879.0	SUP	Nami	267	2521.0	2.0	2.0	7348	16	0	0	2	2	13815	15418	False	RED

get_index = df_normalized_teams['id'].tolist()
index_preproc = np.asarray([[index] * 5 for index in get_index])
index_teams = index_preproc.reshape(len(df_normalized_teams) * 5).tolist()
df_RED['game_id'] = index_teams
df_BLUE['game_id'] = index_teams

df_BLUE[df_BLUE['game_id'] == '2016 International Wildcard Invitational/Scoreboards/Bracket Stage_1_1']

	name	id	role	champion_name	champion_id	gold_15	kills_assists_15	deaths_15	total_gold	total_cs	total_kills	total_monster_kills	total_assists	total_deaths	total_damage_taken	total_damage_dealt	win	side	game_id
165535	Smurf	197966.0	TOP	Trundle	48	5175.0	8.0	3.0	15494	294	0	15	8	3	36337	241203	True	BLUE	2016 International Wildcard Invitational/Score...
165536	PvPStejos	194522.0	JGL	Graves	104	4865.0	11.0	2.0	16049	177	6	148	5	2	25664	231200	True	BLUE	2016 International Wildcard Invitational/Score...
165537	Kira	172113.0	MID	Lissandra	127	5533.0	14.0	2.0	16549	325	3	25	11	2	25513	252607	True	BLUE	2016 International Wildcard Invitational/Score...
165538	Onesh0tiq	188541.0	BOT	Lucian	236	5722.0	13.0	1.0	18339	356	6	15	7	1	15931	251707	True	BLUE	2016 International Wildcard Invitational/Score...
165539	Likkrit	179739.0	SUP	Tahm Kench	223	3386.0	10.0	2.0	12309	80	1	6	9	2	23811	56257	True	BLUE	2016 International Wildcard Invitational/Score...

df_RED[df_RED['game_id'] == '2016 International Wildcard Invitational/Scoreboards/Bracket Stage_1_1']

	name	id	role	champion_name	champion_id	gold_15	kills_assists_15	deaths_15	total_gold	total_cs	total_kills	total_monster_kills	total_assists	total_deaths	total_damage_taken	total_damage_dealt	win	side	game_id
165535	Yang	205635.0	TOP	Maokai	57	4891.0	9.0	2.0	12994	260	1	12	8	2	26978	182014	False	RED	2016 International Wildcard Invitational/Score...
165536	Revolta	195157.0	JGL	Kindred	203	4730.0	6.0	3.0	12638	178	1	122	5	3	27564	195085	False	RED	2016 International Wildcard Invitational/Score...
165537	tockers	201599.0	MID	Ekko	245	5159.0	9.0	2.0	13866	285	5	10	4	2	31698	215422	False	RED	2016 International Wildcard Invitational/Score...
165538	micaO	182405.0	BOT	Jinx	222	5994.0	9.0	5.0	15322	356	3	14	6	5	19697	249792	False	RED	2016 International Wildcard Invitational/Score...
165539	Jockster	169596.0	SUP	Thresh	412	3155.0	6.0	4.0	8906	36	0	0	6	4	18449	20745	False	RED	2016 International Wildcard Invitational/Score...

#Format the data into a df that simulates the one we will receive from the web
def rearrange_df(df):
    df_top = df[['champion_id', 'game_id']][df.role == 'TOP']
    df_jgl = df[['champion_id', 'game_id']][df.role == 'JGL']
    df_top_jgl = pd.merge(df_top.rename(columns={'champion_id': 'TOP'}), df_jgl.rename(columns={'champion_id': 'JGL'}), on='game_id')

    df_bot = df[['champion_id', 'game_id']][df.role == 'BOT']
    df_mid = df[['champion_id', 'game_id']][df.role == 'MID']
    df_bot_mid = pd.merge(df_bot.rename(columns={'champion_id': 'BOT'}), df_mid.rename(columns={'champion_id': 'MID'}), on='game_id')

    df_top_jgl_bot_mid = pd.merge(df_top_jgl, df_bot_mid, on='game_id')

    df_sup = df[['champion_id', 'game_id']][df.role == 'SUP']
    df_top_jgl_bot_mid_sup = pd.merge(df_top_jgl_bot_mid, df_sup.rename(columns={'champion_id': 'SUP'}), on='game_id')
    return df_top_jgl_bot_mid_sup

#Format the data into a df that simulates the one we will received from the web

df_blue = rearrange_df(df_BLUE)
df_red = rearrange_df(df_RED)
data = pd.merge(df_blue, df_red, on='game_id')
df_teams['game_id'] = df_teams['id']

#For the blue side the champions are indexed as _x, red side champions are indexed as _y
full_data = pd.merge(data, df_teams[['winner', 'game_id']], on='game_id')
full_data

	TOP_x	game_id	JGL_x	BOT_x	MID_x	SUP_x	TOP_y	JGL_y	BOT_y	MID_y	SUP_y	winner
0	111	EU Challenger Series/2017 Season/Spring Qualif...	64	202	134	43	57	107	110	13	267	BLUE
1	50	EU Challenger Series/2017 Season/Spring Qualif...	421	81	126	43	98	56	22	134	90	RED
2	111	EU Challenger Series/2017 Season/Spring Qualif...	107	22	61	201	78	121	81	134	43	RED
3	78	EU Challenger Series/2017 Season/Spring Qualif...	164	202	61	90	68	421	110	7	412	BLUE
4	78	EU Challenger Series/2017 Season/Spring Qualif...	2	110	134	267	114	421	15	105	43	BLUE
...	...	...	...	...	...	...	...	...	...	...	...	...
35315	57	IEM Season 11/Gyeonggi/Scoreboards/Playoffs_2_3	421	202	134	432	78	60	81	13	43	RED
35316	78	IEM Season 11/Gyeonggi/Scoreboards/Playoffs_3_1	60	81	69	201	57	421	15	61	43	RED
35317	78	IEM Season 11/Gyeonggi/Scoreboards/Playoffs_3_2	121	22	13	412	111	421	81	61	43	BLUE
35318	78	IEM Season 11/Gyeonggi/Scoreboards/Playoffs_3_3	121	81	69	143	85	421	202	61	21	BLUE
35319	57	IEM Season 11/Gyeonggi/Scoreboards/Playoffs_3_4	64	81	112	201	78	121	202	69	43	BLUE

35320 rows × 12 columns

'''Quick overlook of the data (inpute, scale, encode and balance!)'''
full_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 35320 entries, 0 to 35319
Data columns (total 12 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   TOP_x    35320 non-null  int64 
 1   game_id  35320 non-null  object
 2   JGL_x    35320 non-null  int64 
 3   BOT_x    35320 non-null  int64 
 4   MID_x    35320 non-null  int64 
 5   SUP_x    35320 non-null  int64 
 6   TOP_y    35320 non-null  int64 
 7   JGL_y    35320 non-null  int64 
 8   BOT_y    35320 non-null  int64 
 9   MID_y    35320 non-null  int64 
 10  SUP_y    35320 non-null  int64 
 11  winner   35320 non-null  object
dtypes: int64(10), object(2)
memory usage: 3.5+ MB

Train a logit and test the data

from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector
from sklearn import set_config; set_config(display='diagram')

from transformers import SynergyFeature, RoleFeature, ChampionWinrateFeature

# Paralellize column transformers
preproc = ColumnTransformer([
    ('TOP_rate', RoleFeature('TOP'), ['TOP_x', 'TOP_y']),
    ('SUP_rate', RoleFeature('SUP'), ['SUP_x', 'SUP_y']),
    ('MID_rate', RoleFeature('MID'), ['MID_x', 'MID_y']),
    ('BOT_rate', RoleFeature('BOT'), ['BOT_x', 'BOT_y']),
    ('JGL_rate', RoleFeature('JGL'), ['JGL_x', 'JGL_y'])
    ])

#add model
pipe = make_pipeline(preproc, LogisticRegression(solver='liblinear'))
pipe

Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('TOP_rate',
                                                  RoleFeature(role='TOP'),
                                                  ['TOP_x', 'TOP_y']),
                                                 ('SUP_rate',
                                                  RoleFeature(role='SUP'),
                                                  ['SUP_x', 'SUP_y']),
                                                 ('MID_rate',
                                                  RoleFeature(role='MID'),
                                                  ['MID_x', 'MID_y']),
                                                 ('BOT_rate',
                                                  RoleFeature(role='BOT'),
                                                  ['BOT_x', 'BOT_y']),
                                                 ('JGL_rate',
                                                  RoleFeature(role='JGL'),
                                                  ['JGL_x', 'JGL_y'])])),
                ('logisticregression', LogisticRegression(solver='liblinear'))])

Please rerun this cell to show the HTML repr or trust the notebook.

Same role feature

def Role_DataFrame(df_BLUE, df_RED):
    #merge the dataframe of the blue and red teams champions
    df_BLUE_RED = pd.merge(left=df_BLUE, right=df_RED, left_on= 'game_id', right_on= 'game_id')
    
    #only keep relevant columns
    df_role = df_BLUE_RED[['champion_id_x', 'role_x', 'role_y', 'champion_id_y', 'win_x', 'game_id']]
    
    #times that a given champion played and won or lost against another champion by role
    champion_vs_champion = pd.DataFrame(df_role[['champion_id_x',
                                                 'role_x', 'role_y', 'champion_id_y', 'win_x']].value_counts())

    #times that a given champion played against another champion by role
    total_champion_vs_champion = pd.DataFrame(df_BLUE_RED[['champion_id_x',
                                                           'role_x', 'role_y', 'champion_id_y']].value_counts())

    #percentage that a champion has won or lost against another champion (win(or lost)/ total times played)
    rate_champion_vs_champion = champion_vs_champion.div(total_champion_vs_champion)
    
    #save it as a .csv file -> no need to compute everytime!
    rate_champion_vs_champion.to_csv('role_winrate_champ_vs_champ.csv')
    return  rate_champion_vs_champion

#sent the matrix handling to a dedicated function in a utils.py file
from utils import get_synergy, get_vs_rate, get_winrate
from sklearn.base import BaseEstimator, TransformerMixin

class RoleFeature(BaseEstimator, TransformerMixin):

    def __init__(self, role):
        #get the role winrate champion vs champion DataFrame
        rate_champion_vs_champion = pd.read_csv('role_winrate_champ_vs_champ.csv',index_col=[0,1,2,3,4])
        self.rate_champion_vs_champion = rate_champion_vs_champion
        self.role = role

    def fit(self, X=None, y=None):
        return self

    def transform(self, X, y=None):
        #Get the winrate of the same role champions
        df = X.apply(lambda z: get_vs_rate(z[0], self.role, z[1], self.rate_champion_vs_champion), axis=1)
        return pd.DataFrame(df)

from sklearn.preprocessing import LabelEncoder

y_train = LabelEncoder().fit(full_data.winner).transform(full_data.winner)
X_train = full_data.drop(['game_id', 'winner'], axis=1)

# Train pipeline
pipe.fit(X_train,y_train)

Pipeline(steps=[('columntransformer',
                 ColumnTransformer(transformers=[('TOP_rate',
                                                  RoleFeature(role='TOP'),
                                                  ['TOP_x', 'TOP_y']),
                                                 ('SUP_rate',
                                                  RoleFeature(role='SUP'),
                                                  ['SUP_x', 'SUP_y']),
                                                 ('MID_rate',
                                                  RoleFeature(role='MID'),
                                                  ['MID_x', 'MID_y']),
                                                 ('BOT_rate',
                                                  RoleFeature(role='BOT'),
                                                  ['BOT_x', 'BOT_y']),
                                                 ('JGL_rate',
                                                  RoleFeature(role='JGL'),
                                                  ['JGL_x', 'JGL_y'])])),
                ('logisticregression', LogisticRegression(solver='liblinear'))])

Please rerun this cell to show the HTML repr or trust the notebook.

from sklearn.model_selection import cross_val_score

# Cross validate pipeline
cross_val_score(pipe, X_train, y_train, cv=2, scoring='accuracy').mean()

0.5201019252548131